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The model has been trained on a collection of 28k news articles with tags. Its purpose is to create tags suitable for the given article. We can use this model also for information-retrieval purposes (GenQ), fine-tuning sentence-transformers for asymmetric semantic search.
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<img src="https://upload.wikimedia.org/wikipedia/commons/1/1a/Pieter_Bruegel_d._%C3%84._066.jpg" width="
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Pieter Bruegel the Elder, The Fight Between Carnival and Lent, 1559
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The model has been trained on a collection of 28k news articles with tags. Its purpose is to create tags suitable for the given article. We can use this model also for information-retrieval purposes (GenQ), fine-tuning sentence-transformers for asymmetric semantic search.
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<img src="https://upload.wikimedia.org/wikipedia/commons/1/1a/Pieter_Bruegel_d._%C3%84._066.jpg" width="600"> </br>
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Pieter Bruegel the Elder, The Fight Between Carnival and Lent, 1559
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